import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import warnings
excel_file = r"C:\Users\benor\Desktop\SuperStoreUS-2015.xlsx"
df = pd.read_excel(excel_file)
csv_file = r"C:\Users\benor\Desktop\SuperStoreUS-2015.csv"
df.to_csv(csv_file, index=False, encoding="utf-8")
print(f"CSV dosyası başarıyla kaydedildi: {csv_file}")
df.replace([np.inf, -np.inf], np.nan, inplace=True)
warnings.simplefilter(action='ignore', category=FutureWarning)

CSV dosyası başarıyla kaydedildi: C:\Users\benor\Desktop\SuperStoreUS-2015.csv

df.head()

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1952 entries, 0 to 1951
Data columns (total 25 columns):
 #   Column                Non-Null Count  Dtype         
---  ------                --------------  -----         
 0   Row ID                1952 non-null   int64         
 1   Order Priority        1952 non-null   object        
 2   Discount              1952 non-null   float64       
 3   Unit Price            1952 non-null   float64       
 4   Shipping Cost         1952 non-null   float64       
 5   Customer ID           1952 non-null   int64         
 6   Customer Name         1952 non-null   object        
 7   Ship Mode             1952 non-null   object        
 8   Customer Segment      1952 non-null   object        
 9   Product Category      1952 non-null   object        
 10  Product Sub-Category  1952 non-null   object        
 11  Product Container     1952 non-null   object        
 12  Product Name          1952 non-null   object        
 13  Product Base Margin   1936 non-null   float64       
 14  Country               1952 non-null   object        
 15  Region                1952 non-null   object        
 16  State or Province     1952 non-null   object        
 17  City                  1952 non-null   object        
 18  Postal Code           1952 non-null   int64         
 19  Order Date            1952 non-null   datetime64[ns]
 20  Ship Date             1952 non-null   datetime64[ns]
 21  Profit                1952 non-null   float64       
 22  Quantity ordered new  1952 non-null   int64         
 23  Sales                 1952 non-null   float64       
 24  Order ID              1952 non-null   int64         
dtypes: datetime64[ns](2), float64(6), int64(5), object(12)
memory usage: 381.4+ KB

columns_to_drop = [
    'Row ID', 'Customer ID', 'Customer Name',
    'Product Container',
     'Order Priority', 'Ship Mode',
    'Ship Date', 'Postal Code', 'Country'
]

df = df.drop(columns=columns_to_drop)

df.isnull().sum()

Discount                 0
Unit Price               0
Shipping Cost            0
Customer Segment         0
Product Category         0
Product Sub-Category     0
Product Name             0
Product Base Margin     16
Region                   0
State or Province        0
City                     0
Order Date               0
Profit                   0
Quantity ordered new     0
Sales                    0
Order ID                 0
dtype: int64

df = df.dropna(subset=["Product Base Margin"])

df.shape

(1936, 16)

category_analysis = df.groupby(["Product Category"]).agg(
    Total_Sales=("Sales", "sum"),
    Total_Profit=("Profit", "sum"),
    Quantity_Sold=("Quantity ordered new", "sum")
).reset_index()

category_analysis.head()

category_analysis = category_analysis.sort_values(by="Total_Sales", ascending=False)

plt.figure(figsize=(10,5))
plt.bar(category_analysis["Product Category"], category_analysis["Total_Sales"], color='blue')
plt.xlabel("Ürün Kategorisi")
plt.ylabel("Toplam Satış ($)")
plt.title("Kategori Bazında Satış Dağılımı")
plt.xticks(rotation=45)
plt.show()

category_analysis = category_analysis.sort_values(by="Total_Profit", ascending=False)


plt.figure(figsize=(10,5))
plt.bar(category_analysis["Product Category"], category_analysis["Total_Profit"], color='green')
plt.xlabel("Ürün Kategorisi")
plt.ylabel("Toplam Kâr ($)")
plt.title("Kategori Bazında Kâr Dağılımı")
plt.xticks(rotation=45)
plt.show()

category_analysis = category_analysis.sort_values(by="Quantity_Sold", ascending=False)

# Bar grafiği çizdirme
plt.figure(figsize=(10,5))
plt.bar(category_analysis["Product Category"], category_analysis["Quantity_Sold"], color='orange')
plt.xlabel("Ürün Kategorisi")
plt.ylabel("Satılan Ürün Adedi")
plt.title("Kategori Bazında Satış Miktarı")
plt.xticks(rotation=45)
plt.show()

category_analysis["Profit Margin (%)"] = (category_analysis["Total_Profit"] / category_analysis["Total_Sales"]) * 100

category_analysis = category_analysis.sort_values(by="Profit Margin (%)", ascending=False)

plt.figure(figsize=(10,5))
plt.bar(category_analysis["Product Category"], category_analysis["Profit Margin (%)"], color='purple')
plt.xlabel("Ürün Kategorisi")
plt.ylabel("Kârlılık Oranı (%)")
plt.title("Kategori Bazında Kârlılık Oranı")
plt.xticks(rotation=45)
plt.show()

category_analysis = df.groupby("Product Category").agg({
    "Sales": "sum",
    "Profit": "sum",
    "Discount": "mean"
}).reset_index()

category_analysis.columns = ["Product Category", "Total_Sales", "Total_Profit", "Discount"]
plt.figure(figsize=(10, 6))
plt.scatter(
    category_analysis["Discount"],
    category_analysis["Total_Profit"],
    color=["blue", "green", "red"],
    s=200
)


for i in range(len(category_analysis)):
    plt.annotate(
        category_analysis["Product Category"][i],
        (category_analysis["Discount"][i], category_analysis["Total_Profit"][i]),
        textcoords="offset points",
        xytext=(0, 10),
        ha='center',
        fontsize=10
    )


plt.xlim(0.045, 0.051)  
plt.xlabel("Ortalama İndirim Oranı (%)")
plt.ylabel("Toplam Kâr ($)")
plt.title("Kategori Bazında İndirim ve Kâr İlişkisi")
plt.grid(True)
plt.tight_layout()
plt.show()

product_sales = df.groupby("Product Name")["Sales"].sum().reset_index()

product_sales = product_sales.sort_values(by="Sales", ascending=False)

total_sales = product_sales["Sales"].sum()

product_sales["Cumulative Sales"] = product_sales["Sales"].cumsum()
product_sales["Cumulative Percentage"] = product_sales["Cumulative Sales"] / total_sales * 100

def abc_category(percentage):
    if percentage <= 70:
        return "A"
    elif percentage <= 90:
        return "B"
    else:
        return "C"
product_sales["ABC Category"] = product_sales["Cumulative Percentage"].apply(abc_category)

df = df.merge(product_sales[["Product Name", "ABC Category"]], on="Product Name", how="left")

df.head()

plt.figure(figsize=(8, 5))

sns.set_style("whitegrid")
colors = ["#B0E0FF", "#64A5FC", "#1976D2"]  # Açık-Orta-Koyu mavi

bar = sns.countplot(
    x="ABC Category",
    data=product_sales,
    palette=colors
)

for i in bar.containers:
    bar.bar_label(i, fmt='%d', label_type='edge', padding=2, fontsize=10, weight='bold')

plt.title("ABC Kategorilerine Göre Ürün Sayısı", fontsize=14, fontweight="bold")
plt.xlabel("ABC Kategorisi", fontsize=12)
plt.ylabel("Ürün Sayısı", fontsize=12)

plt.xticks(fontsize=11)
plt.yticks(fontsize=11)
plt.tight_layout()
plt.show()

abc_sales = product_sales.groupby("ABC Category")["Sales"].sum()

abc_sales = abc_sales.reindex(["A", "B", "C"])

colors = ["#B0E0FF", "#64A5FC", "#1976D2"] 


def label_format(pct, allvals):
    absolute = int(round(pct / 100. * sum(allvals)))
    return f"{pct:.1f}%\n(${absolute:,})"

explode = [0.08, 0.02, 0.02]

plt.figure(figsize=(8, 6))
plt.pie(
    abc_sales,
    labels=abc_sales.index,
    autopct=lambda pct: label_format(pct, abc_sales),
    colors=colors,
    startangle=90,
    explode=explode,
    shadow=True,
    wedgeprops={"edgecolor": "black"}
)

plt.title("ABC Kategorilerine Göre Satış Dağılımı", fontsize=14, fontweight="bold")
plt.axis("equal") 
plt.tight_layout()
plt.show()

stock_by_abc = df.groupby("ABC Category")["Quantity ordered new"].sum().reset_index()

plt.figure(figsize=(8, 6))
sns.set_style("whitegrid")

bar = sns.barplot(
    x="ABC Category",
    y="Quantity ordered new",
    data=stock_by_abc,
    palette="crest"
)

for i in bar.containers:
    bar.bar_label(i, fmt='%.0f', label_type='edge', fontsize=10, padding=3)

plt.title("ABC Kategorilerine Göre Toplam Stok Miktarı", fontsize=14, fontweight='bold')
plt.xlabel("ABC Kategorisi", fontsize=12)
plt.ylabel("Toplam Stok Miktarı", fontsize=12)

plt.xticks(fontsize=11)
plt.yticks(fontsize=11)
plt.tight_layout()
plt.show()

fig, ax1 = plt.subplots(figsize=(8, 5))
profit_by_abc = df.groupby("ABC Category")["Profit"].sum().reset_index()

lineplot = sns.lineplot(
    x=profit_by_abc["ABC Category"],
    y=profit_by_abc["Profit"],
    marker="o",
    color="red",
    label="Kâr",
    ax=ax1
)
ax1.set_ylabel("Toplam Kâr ($)", color="red")
ax1.tick_params(axis='y', labelcolor="red")

ax2 = ax1.twinx()
barplot = sns.barplot(
    x=stock_by_abc["ABC Category"],
    y=stock_by_abc["Quantity ordered new"],
    alpha=0.5,
    color="blue",
    label="Stok Miktarı",
    ax=ax2
)
ax2.set_ylabel("Toplam Stok Miktarı", color="blue")
ax2.tick_params(axis='y', labelcolor="blue")

lines_1, labels_1 = ax1.get_legend_handles_labels()
lines_2, labels_2 = ax2.get_legend_handles_labels()
ax1.legend(lines_1 + lines_2, labels_1 + labels_2, loc="upper left")

plt.title("ABC Kategorilerine Göre Karlılık ve Stok Yönetimi Analizi")
plt.show()

a_zarar = df[(df["ABC Category"] == "A") & (df["Profit"] < 0)]
zararli_urunler = a_zarar.groupby("Product Name")[["Sales", "Profit"]].sum().sort_values(by="Profit").reset_index()

top_zarar = zararli_urunler.head(10)

plt.figure(figsize=(10, 6))
sns.barplot(x="Profit", y="Product Name", data=top_zarar, palette="Reds_r")
plt.title("A Kategorisinde En Fazla Zarar Ettiren 10 Ürün")
plt.xlabel("Toplam Zarar ($)")
plt.ylabel("Ürün Adı")
plt.tight_layout()
plt.show()

abc_kar = df.groupby("ABC Category").agg({
    "Profit": "sum",
    "Sales": "sum"
}).reset_index()

abc_kar["Ortalama Kâr Marjı (%)"] = (abc_kar["Profit"] / abc_kar["Sales"]) * 100
abc_kar = abc_kar.sort_values(by="ABC Category")

plt.figure(figsize=(8, 5))
sns.barplot(x="ABC Category", y="Profit", data=abc_kar, palette="Blues_d")
plt.title("ABC Kategorilerine Göre Toplam Kâr")
plt.xlabel("ABC Kategorisi")
plt.ylabel("Toplam Kâr ($)")
plt.tight_layout()
plt.show()

plt.figure(figsize=(8, 5))
sns.barplot(x="ABC Category", y="Ortalama Kâr Marjı (%)", data=abc_kar, palette="coolwarm")
plt.title("ABC Kategorilerine Göre Ortalama Kâr Marjı")
plt.xlabel("ABC Kategorisi")
plt.ylabel("Kâr Marjı (%)")
plt.tight_layout()
plt.show()

df['Order Date'] = pd.to_datetime(df['Order Date'])
monthly_perf = df.groupby(df['Order Date'].dt.to_period("M")).agg({
    'Sales': 'sum',
    'Profit': 'sum'
}).reset_index()
monthly_perf['Order Date'] = monthly_perf['Order Date'].astype(str)
plt.figure(figsize=(14,6))
sns.lineplot(data=monthly_perf, x='Order Date', y='Sales', label='Satış')
sns.lineplot(data=monthly_perf, x='Order Date', y='Profit', label='Kâr')
plt.title('Aylık Satış ve Kâr Trendleri')
plt.xticks(rotation=45)
plt.grid(True)
plt.tight_layout()
plt.show()

df['Order Date'] = pd.to_datetime(df['Order Date'])
df['Order Month'] = df['Order Date'].dt.to_period('M').astype(str)

category_perf = df.groupby(['Product Category', 'Product Sub-Category']).agg({
    'Sales': 'sum',
    'Profit': 'sum'
}).reset_index()


category_perf['Profit Margin (%)'] = (category_perf['Profit'] / category_perf['Sales']) * 100

sales_median = category_perf['Sales'].median()
profit_margin_median = category_perf['Profit Margin (%)'].median()

def segment_category(row):
    if row['Sales'] >= sales_median and row['Profit Margin (%)'] >= profit_margin_median:
        return "Yüksek Satış - Yüksek Kâr"
    elif row['Sales'] >= sales_median and row['Profit Margin (%)'] < profit_margin_median:
        return "Yüksek Satış - Zarar"
    elif row['Sales'] < sales_median and row['Profit Margin (%)'] >= profit_margin_median:
        return "Düşük Satış - Yüksek Kâr"
    else:
        return "Düşük Satış - Zarar"


category_perf['Segment'] = category_perf.apply(segment_category, axis=1)

df = df.merge(category_perf[['Product Sub-Category', 'Segment']], on='Product Sub-Category', how='left')

monthly_segment = df.groupby(['Order Month', 'Segment']).agg({
    'Sales': 'sum',
    'Profit': 'sum'
}).reset_index()


plt.figure(figsize=(14, 6))
sns.lineplot(data=monthly_segment, x='Order Month', y='Sales', hue='Segment', marker="o")
plt.title("Zaman Bazlı Segment Bazında Satış Trendleri", fontsize=14)
plt.xticks(rotation=45)
plt.xlabel("Sipariş Dönemi")
plt.ylabel("Toplam Satış ($)")
plt.grid(True)
plt.tight_layout()
plt.show()

categories = df["Product Category"].unique()

plt.figure(figsize=(12,6))

for category in categories:
    category_sales = df[df["Product Category"] == category].groupby(df["Order Date"].dt.to_period("M"))["Sales"].sum()
    category_sales = category_sales.to_timestamp()
    plt.plot(category_sales.index, category_sales, label=category)

plt.xlabel("Tarih")
plt.ylabel("Toplam Satış ($)")
plt.title("Her Ürün Kategorisi İçin Satış Trendleri")
plt.legend()
plt.xticks(rotation=45)
plt.grid(True)
plt.tight_layout()
plt.show()

df['Order Month'] = df['Order Date'].dt.to_period("M").dt.to_timestamp()
region_month_perf = df.groupby(["Region", "Order Month"]).agg({
    "Sales": "sum",
    "Profit": "sum"
}).reset_index()

plt.figure(figsize=(14, 6))
sns.lineplot(data=region_month_perf, x="Order Month", y="Sales", hue="Region", marker="o")
plt.title("Aylık Satış Trendleri (Bölgelere Göre)", fontsize=14)
plt.xlabel("Sipariş Ayı")
plt.ylabel("Satış ($)")
plt.xticks(rotation=45)
plt.grid(True)
plt.tight_layout()
plt.show()

# Grafik 2: Aylık Kâr Trendleri (Bölgelere Göre)
plt.figure(figsize=(14, 6))
sns.lineplot(data=region_month_perf, x="Order Month", y="Profit", hue="Region", marker="o")
plt.title("Aylık Kâr Trendleri (Bölgelere Göre)", fontsize=14)
plt.xlabel("Sipariş Ayı")
plt.ylabel("Kâr ($)")
plt.xticks(rotation=45)
plt.grid(True)
plt.tight_layout()
plt.show()

product_perf = df.groupby('Product Name').agg({
    'Sales': 'sum',
    'Profit': 'sum'
}).reset_index()

product_perf['Profit Margin (%)'] = (product_perf['Profit'] / product_perf['Sales']) * 100

product_categories = df[['Product Name', 'Product Category']].drop_duplicates()
product_perf = product_perf.merge(product_categories, on='Product Name', how='left')

volume_map = {
    'Furniture': 3,
    'Technology': 2,
    'Office Supplies': 1
}
product_perf['Volume Unit'] = product_perf['Product Category'].map(volume_map)

product_perf['Profit per Unit Volume'] = product_perf['Profit'] / product_perf['Volume Unit']
product_perf['Sales per Unit Volume'] = product_perf['Sales'] / product_perf['Volume Unit']

from IPython.display import display

print(" Alan Başına En Yüksek Kâr Sağlayan Ürünler:")
display(product_perf.sort_values(by='Profit per Unit Volume', ascending=False).head(10)[
    ['Product Name', 'Product Category', 'Profit', 'Volume Unit', 'Profit per Unit Volume']
])

print(" Alan Başına En Yüksek Satış Sağlayan Ürünler:")
display(product_perf.sort_values(by='Sales per Unit Volume', ascending=False).head(10)[
    ['Product Name', 'Product Category', 'Sales', 'Volume Unit', 'Sales per Unit Volume']
])

 Alan Başına En Yüksek Kâr Sağlayan Ürünler:

 Alan Başına En Yüksek Satış Sağlayan Ürünler:

category_volume_perf = product_perf.groupby("Product Category").agg({
    "Profit per Unit Volume": "mean",
    "Sales per Unit Volume": "mean"
}).reset_index()

display(category_volume_perf)

plt.bar(category_volume_perf['Product Category'], category_volume_perf['Profit per Unit Volume'], color='orange')
plt.title('Ürün Kategorilerine Göre Alan Başına Kâr Verimliliği')
plt.xlabel('Ürün Kategorisi')
plt.ylabel('Kâr / Hacim')
plt.grid(True)
plt.tight_layout()
plt.show()

plt.figure(figsize=(10, 5))
plt.bar(category_volume_perf['Product Category'], category_volume_perf['Sales per Unit Volume'], color='skyblue')
plt.title('Ürün Kategorilerine Göre Alan Başına Satış Verimliliği')
plt.xlabel('Ürün Kategorisi')
plt.ylabel('Satış / Hacim')
plt.grid(True)
plt.tight_layout()
plt.show()

region_perf = df.groupby("Region")[["Sales", "Profit"]].sum().reset_index()

plt.figure(figsize=(10, 6))
plt.plot(region_perf["Region"], region_perf["Sales"], marker='o', label='Satış ($)', linestyle='-', color='blue')
plt.plot(region_perf["Region"], region_perf["Profit"], marker='o', label='Kâr ($)', linestyle='--', color='orange')

plt.title("Bölgelere Göre Satış ve Kâr (Çizgi Grafik)")
plt.xlabel("Bölge")
plt.ylabel("Tutar ($)")
plt.grid(True, linestyle='--', alpha=0.5)
plt.legend()
plt.tight_layout()
plt.show()

region_category_perf = df.groupby(["Region", "Product Category"])["Sales"].sum().unstack()

pastel_colors = sns.color_palette("pastel")

region_category_perf.plot(kind="bar", figsize=(12, 6), color=pastel_colors)

plt.title("Bölgelere Göre Kategori Bazlı Satışlar")
plt.xlabel("Bölge")
plt.ylabel("Toplam Satış ($)")
plt.xticks(rotation=0)
plt.legend(title="Ürün Kategorisi")
plt.tight_layout()
plt.grid(True)
plt.show()

loss_by_region = df[df["Profit"] < 0].groupby("Region")["Profit"].sum()

loss_by_region.plot(kind="bar", color="red", figsize=(10,5))
plt.title("Zarar Eden Ürünlerin Bölgelere Göre Dağılımı")
plt.xlabel("Bölge")
plt.ylabel("Toplam Zarar ($)")
plt.grid(True)
plt.tight_layout()
plt.show()

order_count_by_region = df.groupby("Region")["Order ID"].nunique()

order_count_by_region.plot(kind="barh", color="darkblue", figsize=(10,5))
plt.title("Bölgelere Göre Sipariş Sayısı")
plt.xlabel("Sipariş Adedi")
plt.ylabel("Bölge")
plt.tight_layout()
plt.show()

state_profit = df.groupby("State or Province")["Profit"].sum().reset_index()

state_abbrev = {
    'Alabama': 'AL', 'Alaska': 'AK', 'Arizona': 'AZ', 'Arkansas': 'AR',
    'California': 'CA', 'Colorado': 'CO', 'Connecticut': 'CT', 'Delaware': 'DE',
    'Florida': 'FL', 'Georgia': 'GA', 'Hawaii': 'HI', 'Idaho': 'ID',
    'Illinois': 'IL', 'Indiana': 'IN', 'Iowa': 'IA', 'Kansas': 'KS',
    'Kentucky': 'KY', 'Louisiana': 'LA', 'Maine': 'ME', 'Maryland': 'MD',
    'Massachusetts': 'MA', 'Michigan': 'MI', 'Minnesota': 'MN', 'Mississippi': 'MS',
    'Missouri': 'MO', 'Montana': 'MT', 'Nebraska': 'NE', 'Nevada': 'NV',
    'New Hampshire': 'NH', 'New Jersey': 'NJ', 'New Mexico': 'NM', 'New York': 'NY',
    'North Carolina': 'NC', 'North Dakota': 'ND', 'Ohio': 'OH', 'Oklahoma': 'OK',
    'Oregon': 'OR', 'Pennsylvania': 'PA', 'Rhode Island': 'RI', 'South Carolina': 'SC',
    'South Dakota': 'SD', 'Tennessee': 'TN', 'Texas': 'TX', 'Utah': 'UT',
    'Vermont': 'VT', 'Virginia': 'VA', 'Washington': 'WA', 'West Virginia': 'WV',
    'Wisconsin': 'WI', 'Wyoming': 'WY'
}

state_profit["State Code"] = state_profit["State or Province"].map(state_abbrev)

import plotly.express as px

fig = px.choropleth(
    state_profit,
    locations="State Code",  
    locationmode="USA-states",
    color="Profit",
    scope="usa",
    color_continuous_scale="RdBu",
    title="Eyalet Bazlı Kâr Dağılımı (ABD Haritası)"
)


fig.update_layout(
    geo=dict(bgcolor='rgba(0,0,0,0)'),
    height=600,  
    width=1000   
)   

fig.show()

	Row ID	Order Priority	Discount	Unit Price	Shipping Cost	Customer ID	Customer Name	Ship Mode	Customer Segment	Product Category	...	Region	State or Province	City	Postal Code	Order Date	Ship Date	Profit	Quantity ordered new	Sales	Order ID
0	20847	High	0.01	2.84	0.93	3	Bonnie Potter	Express Air	Corporate	Office Supplies	...	West	Washington	Anacortes	98221	2015-01-07	2015-01-08	4.5600	4	13.01	88522
1	20228	Not Specified	0.02	500.98	26.00	5	Ronnie Proctor	Delivery Truck	Home Office	Furniture	...	West	California	San Gabriel	91776	2015-06-13	2015-06-15	4390.3665	12	6362.85	90193
2	21776	Critical	0.06	9.48	7.29	11	Marcus Dunlap	Regular Air	Home Office	Furniture	...	East	New Jersey	Roselle	7203	2015-02-15	2015-02-17	-53.8096	22	211.15	90192
3	24844	Medium	0.09	78.69	19.99	14	Gwendolyn F Tyson	Regular Air	Small Business	Furniture	...	Central	Minnesota	Prior Lake	55372	2015-05-12	2015-05-14	803.4705	16	1164.45	86838
4	24846	Medium	0.08	3.28	2.31	14	Gwendolyn F Tyson	Regular Air	Small Business	Office Supplies	...	Central	Minnesota	Prior Lake	55372	2015-05-12	2015-05-13	-24.0300	7	22.23	86838

	Product Category	Total_Sales	Total_Profit	Quantity_Sold
0	Furniture	619578.75	49515.527436	4912
1	Office Supplies	549681.07	90204.715937	14272
2	Technology	712264.95	75303.156364	5918

	Discount	Unit Price	Shipping Cost	Customer Segment	Product Category	Product Sub-Category	Product Name	Product Base Margin	Region	State or Province	City	Order Date	Profit	Quantity ordered new	Sales	Order ID	ABC Category
0	0.01	2.84	0.93	Corporate	Office Supplies	Pens & Art Supplies	SANFORD Liquid Accent™ Tank-Style Highlighters	0.54	West	Washington	Anacortes	2015-01-07	4.5600	4	13.01	88522	C
1	0.02	500.98	26.00	Home Office	Furniture	Chairs & Chairmats	Global Troy™ Executive Leather Low-Back Tilter	0.60	West	California	San Gabriel	2015-06-13	4390.3665	12	6362.85	90193	A
2	0.06	9.48	7.29	Home Office	Furniture	Office Furnishings	DAX Two-Tone Rosewood/Black Document Frame, De...	0.45	East	New Jersey	Roselle	2015-02-15	-53.8096	22	211.15	90192	C
3	0.09	78.69	19.99	Small Business	Furniture	Office Furnishings	Howard Miller 12-3/4 Diameter Accuwave DS ™ Wa...	0.43	Central	Minnesota	Prior Lake	2015-05-12	803.4705	16	1164.45	86838	B
4	0.08	3.28	2.31	Small Business	Office Supplies	Pens & Art Supplies	Newell 321	0.56	Central	Minnesota	Prior Lake	2015-05-12	-24.0300	7	22.23	86838	C

	Product Name	Product Category	Profit	Volume Unit	Profit per Unit Volume
349	Fellowes PB500 Electric Punch Plastic Comb Bin...	Office Supplies	23865.9042	1	23865.904200
374	GBC DocuBind TL300 Electric Binding System	Office Supplies	11004.4512	1	11004.451200
685	Smead Adjustable Mobile File Trolley with Lock...	Office Supplies	9906.6199	1	9906.619900
408	Global Troy™ Executive Leather Low-Back Tilter	Furniture	18628.1453	3	6209.381767
480	Ibico Hi-Tech Manual Binding System	Office Supplies	5657.5584	1	5657.558400
163	Avery White Multi-Purpose Labels	Office Supplies	4905.3024	1	4905.302400
358	Fellowes Super Stor/Drawer® Files	Office Supplies	4766.7500	1	4766.750000
481	Ibico Ibimaster 300 Manual Binding System	Office Supplies	4568.6073	1	4568.607300
424	Hewlett Packard LaserJet 3310 Copier	Technology	8798.1831	2	4399.091550
608	Okidata ML395C Color Dot Matrix Printer	Technology	7709.3100	2	3854.655000

	Product Name	Product Category	Sales	Volume Unit	Sales per Unit Volume
349	Fellowes PB500 Electric Punch Plastic Comb Bin...	Office Supplies	69013.48	1	69013.480
288	Economy Rollaway Files	Office Supplies	35607.28	1	35607.280
523	Lexmark 4227 Plus Dot Matrix Printer	Technology	61071.73	2	30535.865
685	Smead Adjustable Mobile File Trolley with Lock...	Office Supplies	23995.11	1	23995.110
608	Okidata ML395C Color Dot Matrix Printer	Technology	47611.09	2	23805.545
628	Polycom ViewStation™ ISDN Videoconferencing Unit	Technology	46538.69	2	23269.345
358	Fellowes Super Stor/Drawer® Files	Office Supplies	20478.15	1	20478.150
408	Global Troy™ Executive Leather Low-Back Tilter	Furniture	50871.21	3	16957.070
374	GBC DocuBind TL300 Electric Binding System	Office Supplies	15948.48	1	15948.480
354	Fellowes Staxonsteel® Drawer Files	Office Supplies	14711.35	1	14711.350

📊 Kategori Bazlı Performans Analizi¶

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Amaç:¶

🔍 Analiz Sonuçları ve Gözlemler

	Product Category	Profit per Unit Volume	Sales per Unit Volume
0	Furniture	91.695421	1147.368056
1	Office Supplies	176.525863	1075.696810
2	Technology	174.312862	1648.761458